Collaborative Research: Integrating Antarctic Environmental and Biological Predictability to Obtain Optimal Forecasts

合作研究：整合南极环境和生物可预测性以获得最佳预测

• 批准号: 2037561
• 负责人: Stephanie Jenouvrier
• 金额: $ 54.81万
• 依托单位: Woods Hole Oceanographic Institution
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2037561&HistoricalAwards=false
• 关键词: Collaborative; Research; Integrating; Antarctic; Environmental

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Many biogeochemical and biophysical processes are changing in the present and coming century. The mechanisms and the predictability of these processes are still poorly understood. Limits in understanding of these progress limits climate forecasting. Similarly, ecological forecasting remains a nascent discipline. Comparative assessments of predictability, both within and among species, are critically needed to understand the factors that allow (or prevent) useful ecological forecasts. This study will reveal the influence of climate system dynamics on ecological predictability across a range of scales, and will examine how this role differs among ecological processes, species and regions of Antarctic.The project research will examine the predictability of Antarctic climate and its influence on seabird demographic response, predictability at various temporal and spatial scales, using the longest datasets available for several polar species. Specifically, the PI will 1) identify the physical mechanisms giving rise to climate predictability in Antarctica, 2) identify the relationships between climate and ecological processes at a range of scales, and 3) reveal the factors controlling ecological predictability across a range of scales (e.g., those relevant for short-term adaptive management versus those relevant at end-of-century timescales). These objectives will be achieved using the analysis of existing climate data and century length time-scales, Atmosphere-Ocean Global Circulation Models (AOGCMs), with coupled analysis of existing long-term demographic data for multiple seabird species that span a range of ecological niches, life histories, and study sites across the continent.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。许多生物地球化学和生物物理过程正在发生变化。这些过程的机制和可预测性仍然知之甚少。对这些进展的认识不足限制了气候预测。同样，生态预测仍然是一门新兴学科。对物种内部和物种之间的可预测性进行比较评估，对于了解允许（或阻止）有用的生态预测的因素至关重要。这项研究将揭示气候系统动力学对一系列尺度上的生态可预测性的影响，并将研究这种作用在南极生态过程、物种和地区之间的差异。该项目研究将利用几个极地物种可获得的最长数据集，审查南极气候的可预测性及其对海鸟种群反应的影响，以及在不同时空尺度上的可预测性。具体而言，PI将1)确定导致南极洲气候可预测性的物理机制，2)确定一系列尺度上气候与生态过程之间的关系，以及3)揭示在一系列尺度上控制生态可预测性的因素（例如，与短期适应性管理相关的因素与与世纪末时间尺度相关的因素）。这些目标将通过对现有气候数据和世纪长度时间尺度、大气-海洋全球环流模型（AOGCMs）的分析，以及对现有的多种海鸟物种的长期人口统计数据的耦合分析来实现，这些数据涵盖了整个大陆的一系列生态位、生活史和研究地点。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

The Seasonal-to-Multiyear Large Ensemble (SMYLE) prediction system using the Community Earth System Model version 2

• DOI: 10.5194/gmd-15-6451-2022
• 发表时间: 2022-08
• 期刊: Geoscientific Model Development
• 影响因子: 5.1
• 作者: S. Yeager;N. Rosenbloom;A. Glanville;Xiandu Wu;I. Simpson;Hui Li;M. Molina;K. Krumhardt;Samuel Mogen;K. Lindsay;D. Lombardozzi;W. Wieder;Who M. Kim;J. Richter;M. Long;G. Danabasoglu;D. Bailey;M. Holland;N. Lovenduski;W. G. Strand;Teagan King

Spatio-temporal transferability of environmentally-dependent population models: Insights from the intrinsic predictabilities of Adélie penguin abundance time series

环境依赖性种群模型的时空可转移性：阿德利企鹅丰度时间序列内在可预测性的见解

• DOI: 10.1016/j.ecolind.2023.110239
• 发表时间: 2023
• 期刊: Ecological Indicators
• 影响因子: 6.9
• 作者: Şen, Bilgecan;Che-Castaldo, Christian;Krumhardt, Kristen M.;Landrum, Laura;Holland, Marika M.;LaRue, Michelle A.;Long, Matthew C.;Jenouvrier, Stéphanie;Lynch, Heather J.
• 通讯作者: Lynch, Heather J.